A framework for strengthening agricultural knowledge systems for improved accessibility of agricultural knowledge in Morogoro Region of Tanzania

Abstract

Access to agricultural knowledge has been a serious challenge among farmers in Tanzania. To overcome this, various strategies have been implemented, but few have managed to limit the effect of the problem. The current study was set to design a framework for strengthening agricultural knowledge systems (AKS) with the view to improve access to agricultural knowledge among agricultural stakeholders in Tanzania. Furthermore, the study identified actors in agricultural knowledge systems and determined factors influencing the efficiency of AKS. Quantitative data was collected through questionnaires directed to 314 farmers and 57 AKS actors among agricultural researchers, agricultural extension staff, policy makers, village executives and input suppliers. Findings indicate that individual experience and involvement of actors influence agricultural knowledge creation. Accessibility of agricultural knowledge was influenced by: awareness of knowledge and its sources, ownership of communication tools, affordability of mobile phone tariffs, level of development of knowledge infrastructure, time that radio/TV agricultural programmes were aired, membership in farmers’ groups/associations and provision of agricultural extension services. To strengthen usage of AKS, this study proposes a framework for improving the performance of agricultural knowledge processes and knowledge accessibility. It is hoped that the framework will assist in guiding agricultural actors in sharing knowledge.

Keywords

Agricultural knowledge creation agricultural knowledge systems knowledge accessibility knowledge sharing Tanzania

Introduction and background to the problem

Knowledge is recognised as an important weapon for sustaining a competitive advantage (Lee and Choi, 2003). It has become the major driver of social and economic transformation in the world (Asenso-Okyere and Mekonnen, 2012). Knowledge is a factor of production (Rollet, 2003), which enables people to combine other factors of production more rationally for optimal production. Knowledge is categorised into: know-what knowledge, know-how knowledge and know-why knowledge (King, 2009). Know-what knowledge determines which actions to take when one is presented with a set of stimuli, know-how knowledge is the ability to make correct decisions and know-why knowledge enables the understanding casual relationships (King, 2009).

For knowledge to be used effectively it must be accessed on time. People are custodians of knowledge and major knowledge sources. Knowledge is shared/created when people socialise (Nonaka and Konno, 1998). Knowledge is transferred explicitly or implicitly when people interact (Rollet, 2003). Explicit knowledge is transferred through teaching while implicit knowledge is transferred through observation. Knowledge is transferred between people or as people interact with physical systems. When people interact internalisation, externalisation, combination and socialisation take place (Nonaka and Konno, 1998). Knowledge-rich sources externalise knowledge while those in need of knowledge access and assimilate it. When knowledge is being internalised, there is a possibility of it being combined with the already existing knowledge. Therefore, to make these processes more efficient it is important to link more people together.

In Tanzania, the agriculture sector plays a very important role in the economy. It employs more than 80% of the total population, contributes about 25% to the gross domestic product (GDP), brings about 66% of the foreign exchange and provides raw materials for local industries (URT, 2008, 2013a). Despite the importance of agriculture sector in Tanzania, lack of agricultural support services has been hindering the development of the sector (URT, 2008). Limited access to agricultural knowledge services, along with other factors, hinders the growth of the agricultural sector (Mtega and Benard, 2013; URT, 2008). Despite the importance of the agriculture sector to the economy and livelihoods of majority of Tanzanians, the sector has been performing poorly (URT 2013). Limited access to agricultural knowledge has resulted into irrational decisions on agricultural production and related activities thus dwarfing the sector (Pinda, 2012; URT, 2011). At farm level most farmers have inadequate access to and usage of the most important agricultural knowledge needed for production and post-harvest activities leading to dismal growth of agricultural sector and prevalence of poverty among households whose livelihoods rely solely on agriculture (Lwoga, 2011; Mtega and Benard, 2013). The problem has become more serious because the Government relies on the agricultural extension and advisory systems whose model has become inefficient for years now (CUTS International 2011).

The agriculture sector in Tanzania is characterised by poor research-extension-farmers linkage, low participation of private sector in extension services delivery, insufficient knowledge regarding technological advancements, weak coordination of agricultural extension services and inaccessibility of agricultural knowledge (URT, 2013b). The inaccessibility of agricultural knowledge is more serious in rural areas where most agricultural activities are taking place (Pinda, 2012; URT, 2012). This is exacerbated by the fact that most agricultural research institutes in Tanzania are located in urban or semi urban areas. Inadequate number of agricultural extension staff and lack of facilities to support agricultural extension services has been accelerating the inaccessibility of agricultural knowledge and other information services among actors (CUTS International, 2011). More than 50% of the small-scale farmers in Tanzania have no access to agricultural extension and advisory services (Pinda, 2012). Despite the limited number of agricultural extension agents, studies (Benard et al., 2014) indicate that extension officers are among the most preferred sources of agricultural knowledge. To a great extent this has not only deprived farmers of access to knowledge rights but also has been contributing towards the dwarfing of the sector and increasing poverty prevalence in the country. This calls for a suitable strategy to enhance access to agricultural knowledge among different actors. This study was set to design a framework for strengthening agricultural knowledge systems for improved accessibility of agricultural knowledge in Tanzania.

Conceptual and contextual setting

The creation, storage and sharing of agricultural knowledge involve different actors who work together as a system which is termed the agricultural knowledge system (AKS). This system is defined as a collection of actors in research, extension services, education, training and support systems that act on the knowledge of farmers and generate innovations in response to problems and opportunities, desired outcomes, system drivers and regulatory policies and institutions (Rudman, 2010). The efficiency of AKS depends on how effectively different actors interact with and perform agricultural knowledge management processes.

In Tanzania, access to agricultural knowledge among AKS actors is very limited (Pinda, 2012; URT, 2016). This situation is largely attributed to the weak linkages between research, extension, non-profit organisations, libraries and farmers (Tir, 2006). Moreover, farmers are poorly linked to reliable markets. Thus, this study seeks to design a framework to strengthen the access to agricultural knowledge among actors (AKS usage). The study was guided by the socialisation, externalisation, combination and internalisation (SECI) model (Nonaka et al., 2001).

This study was conducted in the Kilombero, Kilosa and Mvomero districts of the Morogoro region, which is one of the 26 regions of mainland Tanzania. The region is located in the Eastern Agricultural Zone of Tanzania. Agriculture is the main activity in these districts as it employs about 71.4% of the labour force (URT, 2016). These districts are considered to have potential for agricultural activities. However, the National Bureau of Statistics (NBS) (2010) states that they have low penetration of improved agricultural technologies and developments. The high agricultural potential, limited penetration of improved agricultural technologies, ICT infrastructure and agricultural research institutes set the three districts most suitable for this study.

Purpose of the study

The general purpose of this study was to design a framework for strengthening AKS with the view to improve access to agricultural knowledge among actors in Tanzania. The specific objectives were to:

identify factors influencing agricultural knowledge creation;

identify factors influencing agricultural knowledge accessibility;

identify factors influencing the dissemination/sharing agricultural knowledge;

determine factors influencing usage of agricultural knowledge;

suggest a framework for strengthening AKS for improved rural livelihoods in Morogoro region of Tanzania.

Literature review

Agricultural activities require a combination of indigenous and exogenous knowledge. Local people and farmers are custodians of indigenous knowledge; they create such knowledge through experience and manage them through human memory. Most countries have National Agricultural Research Systems (NARS) with a key role of creating exogenous knowledge. NARS is made up of national agricultural research institutes (NARI), agricultural universities, private sector firms, NGOs and farmers’ organisations (FAO, 1996). An effective agricultural knowledge process involves both knowledge sharing and knowledge creation (Yi and Jayasingam, 2012). Thus, when agricultural research institutes exchange knowledge with other stakeholders, new knowledge may be created.

An effective knowledge creation and sharing process needs a ‘ba’ which is a shared space or platform for knowledge creation and sharing (Aslam, 2013; Nonaka and Konno, 1998). The platform can be in either formal or informal settings, and it can enhance interpersonal or mediated communication (Moumouni and Labarthe, 2012). Knowledge creation and knowledge sharing enable organisations and communities to have access to knowledge. ICTs facilitate the creation and sharing of knowledge because they remove the physical constraints of organisations that are not found in a single location, reduce communication and information costs and provide new opportunities to access information (Aker, 2011; Yi and Jayasingam, 2012). Organisations with adequate ICT infrastructure can easily create, share and collaborate with others in the creation and sharing of knowledge.

Despite the importance of knowledge to socio-economic development, the knowledge-creation and knowledge-sharing processes have not been effective in most developing countries. Studies (Lwoga et al., 2011a; Smith and Lumba, 2008) indicate that knowledge infrastructure, knowledge-sharing culture and investment in knowledge creation greatly influence the performance of most knowledge-management strategies. These strategies implemented in developing countries have not been successful due to: limited investment in knowledge infrastructure, low involvement of knowledge users and illiteracy (Gichoya, 2005; Mtega and Malekani, 2009). For example, most developing countries provide agricultural knowledge through agricultural extension services; this strategy has not been successful due to factors such as low number of staff when compared to farmers (CUTS International, 2011; Mbo’o-Tchouawou and Colverson, 2014; Swanson, 2008). To improve the efficiency of agricultural knowledge creation and sharing, all factors influencing the performance of agricultural knowledge processes must be carefully examined and the effects of each assessed.

Scope and research methodology

This study was conducted in the Kilosa, Kilombero and Mvomero districts of Morogoro region. These districts were selected because they have agricultural research institutes, basic ICT infrastructure and potentials for agricultural production. These districts have a higher average yield of staple food crops (URT, 2012) as well as better access to radio and television broadcasts than the other districts in the region. Three wards from each district were purposely selected based on the availability of ICT infrastructure; one village from each ward was then randomly selected. Villages selected were: Michenga, Mgudeni and Mlimba A from the Kilombero district; Rudewa-Batini, Chanzulu and Kimamba B from the Kilosa district; and Wami-Dakawa, Mvomero and Hembeti from the Mvomero district.

This study involved different agricultural stakeholders. A stakeholder analysis was used to determine all actors in the sector. To identify stakeholders and their roles, procedures stipulated by Lelea et al. (2014) were adapted. The first stage was the selection of a human activity system for research focus where a cereal (rice and maize) value chain was selected. The second stage was actor identification and initial characterisation of all actors. The third stage involved determining who has stake in the two crops and the relationship existing between actors. Fifthly, respondents for the study were selected and integrated in the study as described below.

Farmers, agricultural researchers, agricultural extension workers, ward councillors, village executives, agricultural input suppliers, and information service providers were found to be the major stakeholders of maize and rice value chains in Morogoro region. To select respondents from this population, the study employed both probability and non-probability sampling techniques. A sampling frame of farmers from each village was made followed by employing a simple random sampling technique in selecting a sample among farmers from the nine villages. The technique was selected because it can enhance generalization of results. A total of 314 farmers were selected (see Table 1).

Table 1.

Sample size for the study.

S/N	Village	Population size	Working age population	Number of farmers	Anticipated sample size	Response rate
1	Rudewa Mabatini	4876	2389	2126	32	32 (100%)
2	Chanzulu	3617	1772	1578	23	23 (100%)
3	Kimamba B	5967	2924	2602	39	38 (97%)
4	Michenga	4120	2019	1797	28	28 (100%)
5	Mgudeni	8775	4300	3827	57	50 (88%)
6	Mlimba A	7449	3650	3249	49	31 (63%)
7	Hembeti	4010	1965	1749	26	26 (100%)
8	Wami Dakawa	7209	3532	3144	47	39 (83%)
9	Mvomero	9321	4567	4065	61	47 (77%)
Total	55,344	27,118	24,117	362	314 (87%)
Other AKS players involved in the study
Name of other AKS role actors	Number of other AKS role actors selected from each district
Name of other AKS role actors	Kilosa		Kilombero			Mvomero
Agricultural extension staff	3		3			3
Head of the agricultural research outreach section	1		1			1
Head of the agricultural extension unit	1		1			1
Agricultural information service providers	2		2			2
Agricultural inputs suppliers	3		3			3
Buyers of agricultural produce	3		3			3
Village executives	3		3			3
Ward councillors	3		3			3
Total						57

Non-probability sampling technique was employed in selecting respondents among agricultural researchers, agricultural extension workers, ward councillors, village executives, agricultural input suppliers and information service providers. Each head of the agricultural research outreach section (from the three agricultural research institutes), each agricultural extension worker (from all of the nine villages) and each head of the agricultural extension unit (from the three districts) were selected. Based on the scale of their operations, three providers of agricultural information services and three warehouse operators (one from each district) were selected and included in the sample. Moreover, nine agricultural inputs suppliers (one from each village) and nine buyers (one buyer from each village) were included in the sample. Furthermore, all village executives and ward councillors from the nine villages and wards were selected respectively. This made a total of 57 respondents selected among them.

A structured questionnaire was used to collect data from farmers. A survey interview was adopted where face-to-face interviews were used for data collection. This data collection method was more convenient among farmers because it facilitates ease clarification. An unstructured questionnaire was administered among agricultural researchers, agricultural extension workers, ward councillors, village executives, agricultural input suppliers, and information service providers. Face-to-face in-depth interviews were used for collecting data from these stakeholders. Data collected through structured questionnaires were coded and cleaned to make them amenable to analysis. Cleaned data was analysed using the Statistical Package for Social Sciences (SPSS). SPSS facilitated the generation of frequencies, percentages, tables, associations and relationships between variables. Descriptive and inferential statistics were conducted for drawing generalisations and identifying relationships existing between dependent and independent variables. Data collected through unstructured questionnaires were analysed through content analysis and summarised in the form of descriptions and explanations.

Findings and discussions

This section presents the findings of the study followed by the proposed framework for strengthening AKS usage.

Factors influencing creation of agricultural knowledge

The following subsections give details on the influence different factors on agricultural knowledge creation.

Actors’ participation

Actors conduct agricultural knowledge processes; they involve themselves in creating, organising, sharing, disseminating or using agricultural knowledge. Findings indicate that farmers, government and the private sector are the AKS actors. Among AKS actors, government/public sector actors are: agricultural extension officers, agricultural researchers, village executives, ward executives and councillors. Input suppliers, buyers of agricultural produce, and media are mainly from the private sector.

Actors’ participation is expressed by individual involvement in performing agricultural knowledge processes. Findings from researchers indicate that the creation of new knowledge involves different actors. Findings from other AKS actors (agricultural extension workers, ward councillors, village executives, agricultural input suppliers, and information service providers) indicate that they were not actively involved in creation of new knowledge. Moreover, findings indicate that different AKS actors were not linked together. This reduced the level of involvement in performing knowledge related activities. The findings in Table 2 indicate that the participation of AKS actors influences the performance of agricultural knowledge processes, including enhancing its accessibility. Supporting this observation, Nonaka et al. (2001) point out that knowledge creation depends on how different actors socialise, internalise, externalise and combine knowledge. If some of the actors do not participate in implementing agricultural knowledge processes, then the whole process fails (Mangombe and Sabiiti, 2013). Thus, actors’ participation influences agricultural knowledge creation, sharing and usage.

Table 2.

Factors stimulating accessibility of agricultural knowledge.

Factor	Frequency
Accessibility and awareness of knowledge/sources	303 (95%)
Ownership of communication tools	153 (48.7%)
Affordability of mobile phone tariffs	206 (65.6%)
Well-developed ICT infrastructure	279 (88.9%)
Reliable sources of power	297 (94.6%)
Airing radio/TV agricultural programmes during relevant time	160 (51%)
Membership in farmers’ groups/associations	102 (32.5%)
Adequate agricultural extension services	101 (32%)

Individual experience

Farmers were asked if they had been involved in performing different agricultural knowledge processes. A cross-tabulation was run to determine the association between farming experience and performing some agricultural knowledge processes. The findings shown in Table 3 indicate that farming experience has some influence on how individuals perform different agricultural knowledge processes, such as the individual decision to access and use acquired knowledge. Findings in Table 3 further indicate that as experience increases, the need for assistance from a third party decreases. Likewise, findings from other actors reveal that agricultural researchers and agricultural extension staff with more experience are more competent in creating and sharing knowledge respectively.

Table 3.

The influence of farming experience on performing different agricultural knowledge processes.

Years in farming	Used knowledge acquired through farming experience	Requested for agricultural knowledge
1–5	34 (56.7%)	15 (35.7%)
6–10	35 (58.3%)	16 (35.6%)
11–15	29 (72.5%)	12 (42.9%)
16–20	31 (79.5%)	16 (55.2%)
21–25	18 (66.7%)	09 (34.8%)
>25	41 (73.2%)	25 (62.5%)

Factors influencing agricultural knowledge accessibility

Respondents were asked to mention factors that influence the accessibility of agricultural knowledge in their locality. Table 2 indicates that 303 (95%) farmers mentioned that the availability of agricultural knowledge sources influences its accessibility. Actors use easily accessible and readily available knowledge sources. Availability of agricultural knowledge sources is measured by the: affordability of sources, proximity to residential areas, user-friendliness and accessibility. Likewise, ownership of communication tools influences knowledge accessibility, those owning communication tools can easily acquire/share knowledge.

Furthermore, affordable mobile phone tariffs influence agricultural knowledge accessibility. Findings indicate that 206 (65.6%) farmers reported that affordability of mobile phone services influenced agricultural knowledge accessibility. TCRA monitors the mobile phone tariffs in Tanzania. Tariffs set must be just, reasonable, cost oriented, and non-discriminatory (TCRA, 2015). Thus, as tariffs decrease more actors use mobile phones for accessing knowledge.

As reflected in Table 2, well-developed communication infrastructure influences knowledge accessibility. Communication infrastructure includes: roads, power, ICT and other networks facilitating access to knowledge. Poor ICT infrastructure in some rural areas of Tanzania limits agricultural knowledge accessibility.

Table 2 shows that actors did not access agricultural radio/TV programmes broadcast during inappropriate times. Among the farmers, 160 (51%) revealed that broadcasting agricultural radio/TV programmes during relevant times would improve knowledge accessibility. This is supported by scholars (Lwoga et al., 2011a; Siyao, 2012) who found that when agricultural programmes are aired after activities, more actors could access them.

As indicated in Table 2, being a member of networks/groups influences agricultural knowledge accessibility. Farmers (102, 32.5%) reported that membership in farmers’ groups/associations helped them access more agricultural knowledge. Providers of agricultural knowledge services found it easy to reach more farmers when they were in groups rather than as individuals (Duveskog, 2013).

Moreover, Table 2 indicates that agricultural extension services influence agricultural knowledge accessibility. Agricultural extension and advisory services are designed to build and strengthen the capacity of farmers and other stakeholders (Mbo’o-Tchouawou and Colverson, 2014). It is possible to have adequate provision of agricultural extension services only when there are enough providers of these services. Despite having agricultural extension staff in all of the nine villages, findings indicate that only 101 (32%) farmers acknowledged that agricultural knowledge accessibility depended on agricultural extension officers.

Other AKS actors mentioned culture, continuous creation of knowledge and top management support to influence agricultural knowledge accessibility. Community culture is expressed in terms of: leadership, sociability, solidarity, trust, core beliefs, values, norms and social customs (Norizah et al., 2005; Staplehurst and Ragsdell, 2010). Actors’ involvement in performing agricultural knowledge processes was mentioned to influence knowledge accessibility. Involvement is expressed in terms of participation of actors in AKS roles.

The influence of type of AKS on agricultural knowledge accessibility

Actors used human, paper and ICT based AKS for either sharing or accessing agricultural knowledge. A cross-tabulation was run to determine how the demographic characteristics of respondents influenced their choice of type of AKS. Findings in Table 4 indicate that most farmers (310, 98.7%) used human-based AKS because of the availability of its components. Human-based AKS involves human experience for knowledge creation/acquisition, human memory for knowledge storage and face-to-face communication for knowledge sharing (Lwoga et al., 2011a).

Table 4.

Influence of demographic characteristics on AKS usage (N=314).

Demographic variables		Type of AKS
Demographic variables		Human-based AKS	Paper-based AKS	ICT-based AKS
Sex	Male	151 (98.7%)	26 (17%)	125 (81.7%)
Sex	Female	159 (98.8%)	13 (08.1%)	107 (66.5%)
Age group	15–25	22 (100%)	00 (00%)	18 (81.9%)
	26–35	78 (98.7%)	07 (08.9%)	56 (70.9%)
	36–45	79 (98.8%)	14 (17.5%)	59 (73.8%)
	46–55	42 (100%)	06 (14.3%)	36 (85.7%)
	56–65	89 (100%)	12 (13.5%)	63 (70.8%)
Level of education	Informal	38 (100%)	02 (05.3%)	14 (42.1%)
	Adult	13 (92.9%)	02 (14.3%)	10 (71.4%)
	Primary	217 (98.6%)	29 (13.2%)	170 (77.3%)
	Secondary	42 (100%)	06 (14.3%)	36 (85.7%)

As reflected in Table 4, ICT-based AKS follows in level of usage. Its usage is limited by: poor ICT networks; low ownership of ICT tools; illiteracy; poor power supply; limited ownership of ICT tools; lack of two-way communication and airing programs during odd hours (Tables 3 and 4). Moreover, the findings in Table 4 indicate that usage of ICT- and paper-based AKS was influenced by the level of education of the actors.

Other factors that limit AKS usage are: lack of feedback, low ownership of ICT tools and not being a member of farmers’ group/associations (see Table 5). Poor ICT network/signals, poor power supply and airing radio/TV programmes during odd hours also limit AKS usage.

Table 5.

Factors hindering usage of ICT-based AKS (N=314).

Reason for limited usage of ICT-based AKS	Frequency distribution
Feedback not easily made	10 (3.2%)
Do not own communication tools	114 (36.3%)
Not a member of a farmers’ group	262 (83.4%)
Poor network/signal of some ICTs	26 (8.3%)
Poor/lack of power supply	125 (39.8%)
TV and radio programmes aired during odd hours	123 (39.2%)

Findings in Table 6 indicate that among mobile phone applications, voice calls were used most often for accessing/sharing agricultural knowledge because it provides immediate feedback. Regardless of the knowledge category, more farmers used voice calls to access/share agricultural knowledge (Table 6) while few used SMS. This is explained by the fact that farmers either did not know how to use the SMS application or feared another’s failure to respond to a sent SMS.

Table 6.

Using mobile phone applications to acquire/share agricultural knowledge.

Knowledge category	Frequency distribution
Knowledge category	Voice calls	SMS
Weather	107 (67.3%)	29 (18.2%)
Seed selection techniques	110 (69.2%)	38 (23.9%)
Crop maintenance	49 (30.8%)	23 (14.5%)
Post-harvest practices	29 (18.2%)	17 (10.7%)
Agricultural marketing	45 (28.3%)	14 (8.8%)
Agricultural credit	03 (1.9%)	01 (0.62%)

Other actors used all types of AKS for creation, sharing or accessing agricultural knowledge. The type of AKS had limited influence on accessing agricultural knowledge among other AKS actors. They used any type of AKS so as to perform intended agricultural knowledge processes.

Efforts exerted to acquire and share knowledge

In order to acquire/share agricultural knowledge, actors must exert some effort, which can either be in the form of money spent or skills needed. It can also be the ability to own and manage communication tools. As indicated in Table 7, the Phi value of -0.029 indicates that there is a negative association between high mobile phone tariffs and using mobile phones for acquiring/sharing knowledge. Thus, those who do not have enough funds may fail to own and use communication tools for agricultural purposes.

Table 7.

Strength of association between tariffs and usage of mobile phones (N=314).

Tariffs against usage	Type of variable	Measure	Value	Approx. Sig.
Affording mobile phone tariffs * Using mobile phones for acquiring/sharing agricultural knowledge	Nominal by Nominal	Phi	−.029	.657
	Nominal by Nominal	Cramer’s V	.029	.657
No. of valid cases			234

Efforts exerted to acquire and share knowledge had a limited influence among the majority of other AKS actors. These actors have the needed skills and resources for accessing and sharing agricultural knowledge.

Behavioural intention

Behavioural intention is either directly or indirectly influenced by level of education, age, sex, experience and income. These traits influence the perceived ease of use and usefulness of AKS. They may help individuals understand what they can achieve after using knowledge and the amount of effort needed for accessing/sharing agricultural knowledge.

As indicated in Table 8, the level of education influences knowledge acquiring/sharing. Behavioural intention to acquire/share knowledge increases along with an increase in level of education. Behavioural intention may influence the usage of some communication tools. Findings in Table 4 indicate that the age and level of education can influence AKS usage. Moreover, findings in Table 2 indicate that usage of ICT-based AKS decreases with age. This is techno-phobia which, in most cases, decreases with an increase in age. Technophobia is the fear of the use of some technologies (Hutchby and Moran-Ellis, 2013). However, usage of ICT-based AKS increased with an increase in the level of education of actors (Table 2).

Table 8.

Influence of level of education on intention to acquire/share agricultural knowledge.

Level of education	Frequency
Level of education	Yes	No
Informal education	22 (56.9%)	17 (43.1%)
Adult education	08 (57.1%)	06 (42.9%)
Primary education	166 (75.8%)	53 (24.2%)
Secondary education	40 (95.2%)	02 (4.8%)
Total	236 (75.4%)	78 (24.6%)

Availability of agricultural knowledge

The availability of agricultural knowledge influences knowledge accessibility and sharing. As indicated in Table 2, agricultural knowledge accessibility is explained by ease of access of: agricultural knowledge sources; ownership of communication tools; affordability of tariffs; well-developed ICT infrastructure and reliable sources of power. It is also explained by relevancy of broadcast time for agricultural radio/TV programmes, membership in farmers’ groups/associations or professional networks and adequate agricultural extension services.

Community culture

Effective implementation of agricultural knowledge processes depends on the community culture. Culture shapes how people live and interact among themselves. Community culture is expressed in terms of: leadership, sociability, solidarity, trust, core beliefs, values, norms and social customs (Norizah et al., 2005; Staplehurst and Ragsdell, 2010).

As reflected in Table 9, fellow farmers were major recipients of knowledge among farmers because the decision on where to access/share knowledge is influenced by culture. Moreover, organisation/professional culture influenced other AKS actors in performing several agricultural knowledge processes.

Table 9.

Recipients of agricultural knowledge (N=314).

Recipients	Frequency distribution
Agricultural extension agents	82 (28.1%)
Fellow farmers	281 (96.2%)
Village executives	16 (5.5%)
Agricultural researchers	11 (3.8%)
Input suppliers	68 (17.5%)
Buyers	27 (9.2%)
Farmers’ group	39 (13.4%)
Village-based agricultural advisor	74 (25.3%)

Knowledge infrastructure

Findings in Table 5 show that ICT networks, power reliability and road infrastructure influence agricultural knowledge accessibility. Knowledge infrastructure influences knowledge accessibility among actors regardless of their role in AKS. Kamba (2009) also reports that reliable electricity, telecommunication, roads and transportation enhance access to knowledge.

Ownership of communication tools

Communication tools, particularly ICTs, enhance the creation, sharing, storage and dissemination of agricultural knowledge. As reflected in Table 10, actors either owned or accessed some communication tools from a third party. Therefore, owners of ICT tools can easily use them for agricultural knowledge purposes.

Table 10.

Access points of communication tools.

ICT tool	Frequency distribution (within using specific ICTs)
ICT tool	Own	Relative	Friend	Kiosk/club
Radio set	207 (97.2%)	04 (1.9%)	02 (0.94%)	00 (00%)
TV set	63 (75%)	15 (17.9%)	04 (4.8%)	02 (2.4%)
Mobile phone	199 (99%)	02 (01%)	00 (00%)	00 (00%)

Types of channels for accessing/sharing agricultural knowledge

Agricultural knowledge is accessed/shared through different channels. Table 11 indicates that farmers accessed/shared knowledge through face-to-face communication, SMS, voice calls and village meetings. Most farmers (192, 61.1%) accessed agricultural knowledge through face-to-face communication. This is partly explained by the fact that most farmers (281, 96.2%) share knowledge with fellow farmers (see Table 11).

Table 11.

Channels used to access/share agricultural knowledge (N=314).

Channel used	Frequency distribution
Face-to-face oral communication	192 (61.1%)
SMS	38 (12.1%)
Voice calls	110 (35%)
Village meetings	92 (29.3%)

Others access/share agricultural knowledge through voice calls (110, 35%) and SMS (38, 12.1%) because mobile phones are found to be among the most owned ICTs. Wyche and Steinfield (2015) support that voice calls are used more than SMS because of fear of sending messages and not knowing whether they would be received/replied to. Findings also indicate that few farmers (92, 29.3%) access/share knowledge through village meetings.

Other AKS actors access/share knowledge through face-to-face communication, mobile phones, internet, print resources and radio/TV. They used mediated and unmediated communication channels. Mediated tools facilitate audio/audio-video oral communication. Mobile phones were used to access/share knowledge with: farmers’ representatives; representatives from private companies; agricultural extension officers; village and ward executives as well as buyers and input suppliers. They use print materials along with radio/TV programmes to share knowledge to a wider audience.

Factors influencing usage of agricultural knowledge

According to Mtega and Benard (2013), factors limiting access to and usage of knowledge include: lack or inadequacy of knowledge infrastructure and few agricultural knowledge sources including agricultural knowledge service providers. Farmers were asked to mention reasons which limited them from using acquired agricultural knowledge. Findings in Table 12 indicate that 21 (6.7%) of the farmers did not use acquired knowledge because they did not afford to buy some of the inputs while 30 (9.6%) perceived some categories of agricultural knowledge to be useless and 145 (46.2%) of the farmers did not use some agricultural knowledge because they acquired it late. It was also found that 56 (17.8%) of the farmers did not put into use acquired agricultural knowledge because some of the inputs were not available or delivered late. Finally, 35 (11.1%) of them did not use some of the acquired agricultural knowledge because it was time consuming to put into use the acquired skills.

Table 12.

Factors limiting usage of acquired agricultural knowledge (N=314).

Factor	Frequency distribution
Did not afford to acquire inputs	21 (6.7%)
Found knowledge to be useless	30 (9.6%)
Acquired agricultural knowledge late	145 (46.2%)
Inputs were not available/delivered late	56 (17.8%)
Time-consuming to use knowledge	35 (11.1%)

Social influence

The manner in which AKS actors perform certain activities as they interact with peers, colleagues or supervisors is called social influence. Moussaïd et al. (2013) report that social influence works under the expert and majority effect. As reflected in Table 13, farmers used most of the acquired knowledge from fellow farmers due to social influence.

Table 13.

Social influence and usage of acquired agricultural knowledge.

Agricultural knowledge Source	Frequency distribution
Agricultural knowledge Source	Acquired knowledge from the source N=314	Used acquired knowledge from the source N=61
Agricultural extension	102 (32.5%)	18 (29.5%)
Fellow farmers	305 (97.1%)	58 (95.1%)
Farmers’ group/associations	63 (20.1%)	12 (19%)
Village-based agricultural advisor	120 (38.2%)	14 (11.7%)

Among the farmers, most of the effort is exerted by fellow farmers. Findings from other AKS actors indicate that expert effect is exerted by supervisors to subordinates. Therefore, both expert and majority effects have positive impacts on usage of AKS.

Perceived usefulness of agricultural knowledge

As indicated in Table 14, AKS actors acquire/share knowledge perceived to be useful. Thus, actors consult AKS to acquire/share knowledge so as to meet needs. As reflected in Table 14, perceived usefulness of agricultural knowledge is determined by the frequency of acquiring knowledge.

Table 14.

Frequency of acquiring/sharing agricultural knowledge (N=314).

Agricultural knowledge category	Frequency distribution
Agricultural knowledge category	Very frequently	Frequently	Infrequently	Not at all
Weather	147 (46.8%)	44 (14%)	20 (6.4%)	103 (32.8%)
Farm preparation	0.0 (0.0%)	35 (11.15%)	48 (15.3%)	231 (73.6%)
Seed selection techniques	131 (41.7%)	137 (43.6%)	16 (5.1%)	30 (9.6%)
Seed sowing techniques	0.0 (0.0%)	38 (12.1%)	45 (14.3%)	231 (73.6%)
Crop maintenance	105 (33.4%)	83 (26.4)	27 (8.6%)	99 (31.5%)
Post-harvest practices	18 (5.7%)	49 (15.6%)	47 (15%)	200 (63.7%)
Agricultural marketing	26 (8.3%)	36 (11.5%)	51 (16.2%)	201 (64%)
Agricultural credits	3 (1.0%)	11 (3.5%)	29 (9.2%)	271 (86.3%)

Table 14 indicates that more farmers frequently share/acquire agricultural knowledge related to weather (147, 46.8%), seed selection techniques (131, 41.7%) and crop maintenance (105, 33.4%). The frequency in which other knowledge categories were shared, was determined by perceived usefulness.

AKS actors acquire knowledge which is perceived to be useful. They acquire the agricultural knowledge that is needed by themselves or by farmers because in most cases they act as knowledge breakers.

The proposed framework for strengthening AKS usage

The study proposes a framework for strengthening AKS usage, hence increased usage of agricultural knowledge among actors. The proposed framework is based on seven independent and two dependent variables.

Independent variables influencing AKS usage

The independent variables influencing AKS usage are: knowledge factors, type of AKS, involvement of actors, individual, institutional, agricultural production and communication factors.

Knowledge factors

Knowledge factors influence the usage of AKS and thus the usage of agricultural knowledge among actors. These factors are more related to knowledge as an item that actors are willing to use. They include: knowledge usefulness, availability, accessibility, ease of use, awareness on knowledge, affordability in accessing knowledge and timeliness in accessing it.

Before using agricultural knowledge, one must perceive its importance. Perceived usefulness is determined by expressed agricultural knowledge needs, relevancy of agricultural knowledge to purpose as well as acquiring and using acquired agricultural knowledge. Knowledge needs can be defined as people’s personal needs for knowledge (Tong and Ayres, 2009). In this study, perceived usefulness of knowledge was revealed by acquiring and using agricultural knowledge (Table 7 and Figure 1). Perceived usefulness of agricultural knowledge is influenced by an individual’s level of education, farming experience and age. Thus, perceived usefulness of knowledge has a direct influence on knowledge creation, sharing and usage.

Figure 1.

Framework for strengthening AKS usage.

AKS usage depends on agricultural knowledge availability and accessibility (Table 2 and Figures 1). Studies (Civelek et al., 2015; Vaseegaran, 2014) support this by reporting that the success of firms depends on knowledge availability. It is expressed by accessibility of knowledge sources, availability of feedback, awareness of the existence of these sources, awareness of availability and awareness of when a source can be accessed. When actors believe AKS has the needed knowledge, they tend to use it for agricultural knowledge processes.

Usage of agricultural knowledge depends on the ease of using it. This refers to how easy an individual believes the system is to use (Kasim, 2015). In AKS usage, ease of use is the amount of effort exerted in implementing knowledge processes. Efforts exerted may be in the form of money or time spent for creating and acquiring, sharing and disseminating or storing and organising agricultural knowledge (Table 5). Findings in Table 8 indicate that not all actors could afford to acquire/share agricultural knowledge due to high tariffs and lack of funds. Moreover, the findings in Table 3 indicate that age and experience of farmers influence the usage of some AKS and agricultural knowledge sources. Furthermore, findings in Tables 3 and 5 indicate that actors either managed or failed to acquire/share agricultural knowledge due to illiteracy. Therefore, ease of use of knowledge is influenced by individual factors such as the level of education, farming experience and income.

Usage of AKS is influenced by awareness of the availability of agricultural knowledge. Findings in Table 2 indicate that some actors were hindered from using AKS because they were unaware that the needed knowledge was available. Awareness of agricultural knowledge is influenced by level of education, experience and membership in agriculture-related groups (individual factors). Timeliness in delivery of knowledge, ownership of communication tools and affordability of fees to acquire some categories of agricultural knowledge (Tables 3 and 7).

Individual factors

Individual factors influence AKS usage. These factors include: level of education, experience, income, occupation and organisational membership. Level education influences individual perception, decision-making and ability to use technologies. Level of education can directly influence AKS usage through perceived usefulness, ease of use, ability to use communication tools and ability to understand languages (Table 4). Likewise, one’s experience in agricultural activities influences usage of AKS. Experience is measured by number of years one has been involved in farming. Findings in Table 3 indicate farmers’ experience in the field influenced choice of agricultural knowledge sources and creation of agricultural knowledge. Among agricultural researchers, involvement in creating new agricultural knowledge was found to be higher among experienced agricultural researchers than juniors.

Level of income influences AKS usage as well. It is through income communication that infrastructure can be developed, used and maintained. Moreover, accessing knowledge sources and using some communication channels may require some fees (Table 8). Actors in AKS may form agricultural networks or be members of agricultural associations/groups/organisations. Findings in Table 2 indicate that farmers’ associations/groups were important sources of agricultural knowledge.

Actors’ involvement/participation

Actors involve themselves in performing different knowledge processes (Lee and Yang, 2000). When not actively involved, performance of knowledge processes becomes ineffective (Mangombe and Sabiiti, 2013). Due to this fact, involvement of all AKS actors in agricultural knowledge processes is inevitable. Findings from this study indicate that linkage between AKS actors was poor, thus dwarfing the level of creation, sharing and usage knowledge. Therefore, involvement of all AKS actors influences AKS performance and has a direct influence on AKS usage and is determined by actors’ community culture and literacy level.

Communication factors

Communication factors have a strong impact on AKS usage. These factors include: communication infrastructure (ICT and road networks and power supply); accessibility of tools/network; ease of use; media/channels; language and feedback mechanism. Electricity, telecommunication, roads and transportation enhance access to knowledge (Wyche and Steinfield, 2015). Findings in Table 5 indicate that poor ICT networks and poor power supply limit ICT usage. Moreover, the usefulness of the ICT networks is influenced by the accessibility of ICT tools (communication tools) and the ease of use of such tools. Furthermore, channels/media are important for effective communication. Findings in Table 11 indicate that actors access and share knowledge through face-to-face oral communication, SMS, voice calls village meetings. Findings from agricultural actors who are not directly involved in farming, access and share knowledge through: face-to-face communication; mobile phones; radio and TV broadcasts; leaflets/brochures; notes as well as internet. Easily used communication channels influence the level of usage of AKS. Channels which facilitate immediate feedback are considered to be more effective (Table 5). It is for this reason that more farmers prefer to use voice calls to SMS (Table 6). In AKS, simple and understandable language plays an important communication role.

Institutional factors

Institutional factors enhance AKS and agricultural knowledge usage. These factors include the agricultural policy, laws, regulations and culture. Findings from this study indicate that the Government develops the agricultural policy and sets laws and regulations to be followed by actors. To implement the agricultural policy, these laws and regulations are set and put into action that agricultural knowledge is created, disseminated and used for improved agricultural production. Therefore, good agricultural policies, laws and regulations are important for effective AKS. Likewise, organisational culture shapes how people live. Findings from this study indicate that due to community culture (strong sociability, solidarity and trust) actors acquired agricultural knowledge from some sources and shared them with some recipients (Tables 10 and 14). Therefore, institutional factors have a direct influence on AKS usage and an indirect influence through involvement of actors. Individual factors (level of education and experience) moderate the influence of institutional factors on AKS usage.

Agricultural input factors

Agricultural input factors enhance AKS and agricultural knowledge usage among actors. They are expressed in terms of an input’s accessibility, affordability, ease of use and delivery time. Findings in Tables 2 and 4 indicate that if actors can not afford to buy agricultural input then they do not acquire knowledge related thereto. Likewise, findings indicate that actors do not put into use acquired agricultural knowledge when the inputs are not available/delivered timeously. If usage of some agricultural inputs requires more skills/knowledge, then only a few actors may acquire such inputs and the associated knowledge (Table 9). Therefore, timely access to affordable agricultural input may influence AKS and agricultural knowledge usage. Individual factors (level of education, income, experience and organisational membership) moderate the influence of agricultural input factors on AKS usage.

Type of AKS

Actors use human-based, paper-based and ICT-based AKS. Findings in Table 4 indicate that most farmers used human-based AKS, followed by ICT-based while paper-based is least. Human-based AKS is traditional and in most cases involves face-to-face communication. It also involves: human experience for knowledge creation and acquisition; human memory for knowledge storage and face-to-face oral communication for knowledge sharing (Lwoga et al., 2011a). Therefore, cheap and simple AKS is used more, as the type of AKS available for use influences the level of usage of the system. It has a direct influence on level of usage of system. Individual factors (age, level of education and income) moderate its influence on AKS usage.

Dependent variables for the framework

The two dependent variables for the proposed framework are AKS usage and performance of agricultural knowledge processes. Details of each are found below.

Performing agricultural knowledge processes

AKS usage is a dependent variable influenced by: knowledge factors, type of AKS, involvement of actors, individual, institutional, agricultural production and communication factors. Individual factors influence the usage of AKS and moderate the influence of other independent variables on AKS usage. Institutional factors shape and monitor how actors involve themselves in AKS. Generally, the efficiency of AKS depends on how all seven factors are set to enhance improved performance of the agricultural sector.

The performance of different agricultural knowledge processes depends on the level of usage of AKS. The more AKS is used the more the performance of agricultural knowledge processes (acquiring, capturing, creating, storing, organising, retrieving, using, sharing and reusing agricultural knowledge) is improved. As agricultural knowledge processes take place, different variables of the AKS model can be re-involved, thus increasing AKS usage and the level of performance of agricultural knowledge processes. For effective performance of agricultural knowledge, using and reusing AKS is inevitable.

Improved accessibility and usage of agricultural knowledge

Increased performance of agricultural knowledge processes leads to accessibility and usage of agricultural knowledge among AKS actors. Studies (Kremp and Mairesse, 2004; Lwoga et al., 2011b) indicate that access to and usage of relevant information and knowledge is very important to improve the agricultural performances and livelihoods. Therefore, accessibility and usage of agricultural knowledge is one of the important factors of production.

Conclusion and recommendations

This study aimed to design a framework for strengthening AKS with the view to improving access to agricultural knowledge among actors in Tanzania. To enhance access to agricultural knowledge, all actors involved in creation, sharing and usage of knowledge must interact continuously. Moreover, involving more actors in creation and sharing/dissemination of agricultural knowledge can improve accessibility of agricultural knowledge.

So as to improve accessibility of agricultural knowledge, actors are recommended to work closely together. Providers of agricultural knowledge should conduct agricultural knowledge needs assessment and provide the required knowledge on time. The Government, in partnership with the private sector, should increase the level of investment in the knowledge infrastructure and provide more resources to enhance agricultural knowledge creation and sharing. The study has also proposed a framework for strengthening AKS so as to increase the accessibility of agricultural knowledge. It is recommended that the framework is tested before being adopted in agricultural knowledge processes.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mpho Ngoepe

Author biographies

Wulystan Pius Mtega is senior lecturer in the Department of Reference and Community Information Services at Sokoine University of Agriculture in Tanzania. He holds PhD in Information Science from the University of South Africa. Dr Mtega is involved in teaching, research and consultancy activities. His areas of interests are knowledge management and information and communication technology (ICT) for development. He has written several publications in knowledge management and ICT for development.

Mpho Ngoepe is a professor in the Department of Information Science at the University of South Africa (Unisa). Prior to his current position at Unisa, professor Ngoepe has worked for the United Nations Children’s Fund, Auditor-General South Africa and the National Archives of South Africa. professor Ngoepe has served on the national committee of the South African Society of Archivists (2009–2017) and the board of Eastern and Southern Regional Branch of the International Council on Archives (2009–2017) as the editor of the journals. He also serves on the advisory council of the National Archives of South Africa in his capacity as the chairperson of Gauteng Provincial Archives. He is the director of the African Team for the multi-national, interdisciplinary research project exploring issues concerning digital records called the International Research on Permanent Authentic Records in Electronic Systems (InterPARES Trust) (2013–2018).

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